Speech-assisted intelligent software architecture based on deep game neural network

摘要

With the rapid development of Internet technology, network assisted instruction system develops rapidly. Speech recognition is a technology that transforms the speech from human beings into words or symbols. From the acoustic feature extraction 40 years ago to the automatic speech recognition system based on deep neural network, speech recognition technology has been gradually improved. Speech recognition algorithm based on neural network has great potential and is an effective way to solve the bottleneck problem of existing speech recognition algorithm. In this paper, the deep neural network and game theory are combined to reduce the dimension of the model by singular value decomposition and reconstruction, in order to reduce the amount of data and improve the accuracy of the experiment. The proposed framework is implemented on the Android system to validate the performance. Compared with the state-of-the-art methodologies, the proposed system can effectively recognize the speech information and extract the information structure.